Modelling Online Complaining Behaviour in The Hospitality Industry: an Application of Data Mining Algorithms

EasyChair Preprint 6838

Abstract

This study aims to enrich literature on Big Data Analytics and Data Mining Algorithms to the field of hospitality and tourism industry by predicting the complaint attributions significantly differing from various hotel classes (i.e. higher star-rating and lower star-rating) of travelers related to their online complaining behavior. For this, Decision Tree (i.e. CHAID Algorithm) was conducted. Findings revealed that guest from higher star-rating hotels are most likely to give online complaints on: i) Service Encounter and were stayed at large size hotel; ii) Value for Money, were also complained on Service Encounter, and were stayed at medium size hotel; iii) Room Space, were also complained on Service Encounter, and were stayed at small size hotel. Additionally, guests of lower star-rating hotel are most likely to give online complaint on Cleanliness, but not Value for Money, Room Space, and Service Encounter, and are stayed at small size hotel.

Keyphrases: Data Mining Algorithms (DMAs), Decision Tree (DT), Hotel class, Online Complaining Attributes (OCAs), Online Complaining Behavior (OCB)

@booklet{EasyChair:6838,
  author    = {Raksmey Sann and Pei-Chun Lai and Shu-Yi Liaw and Chi-Ting Chen},
  title     = {Modelling Online Complaining Behaviour in The Hospitality Industry: an Application of Data Mining Algorithms},
  howpublished = {EasyChair Preprint 6838},
  year      = {EasyChair, 2021}}